39 Dimensionality and sample size considerations in pattern recognition practice

Jain, Anil K.; Chandrasekaran, B.

doi:10.1016/s0169-7161(82)02042-2

Cited by 413 publications

(232 citation statements)

References 50 publications

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“…Theoretically, if the number of training data is small compared to the size of the feature vectors, the classifier will most probably give poor results [20]. It is recommended to use at least five to ten times as many training samples per class as the dimensionality [21], [22]. In this paper, the SRS is used twice to gain 50 cases from each channel EEG data.…”

Section: Feature Extractionmentioning

confidence: 99%

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

Wang¹,

Zhu²,

Li³

et al. 2014

IJBBB

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Section: Feature Extractionmentioning

confidence: 99%

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

Wang¹,

Zhu²,

Li³

et al. 2014

IJBBB

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“…The problem of selecting most representative feature attributes, commonly known as dimension reduction, has been examined by principal component analysis (PCA) in many pattern recognition areas [8]. The basic idea of PCA is to find m linearly transformed components so that they explain the maximum amount of variances in the input data.…”

Section: Global Features and Distance Measurementioning

confidence: 99%

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

Rahman

Desai

Bhattacharya

2006

2006 9th International Conference on Information Fusion

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“…Actually, the obtained LFV's can not only encode the appearance of local regions but somehow preserve the spatial configuration of 2D face image as well. In addition, for high dimensional data, image partition is a powerful way to address the curse of dimensionality [2].…”

Section: Multiple Som-based Representationmentioning

confidence: 99%

Recognition from a Single Sample per Person with Multiple SOM Fusion

Tan

Jun

Chen

2006

Advances in Neural Networks - ISNN 2006

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Abstract. One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples, and many existing face recognition techniques rely heavily on the size and representative of training set. Those algorithms may suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. In this paper, we present a multiple-SOMs-based fusion method to address this problem. Based on the localization of the face, multiple Self-Organizing Maps are constructed in different manners, and then fused to obtain a more compact and robust representation of the face, through which the discrimination and class-specific information can be easily explored from the single training image among a large number of classes. Experiments on the FERET face database show that the proposed fusion method can significantly improve the performance of the recognition system, achieving a top 1 matching rate of 90.0%.

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39 Dimensionality and sample size considerations in pattern recognition practice

Cited by 413 publications

References 50 publications

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

Recognition from a Single Sample per Person with Multiple SOM Fusion

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